Science - USA - 03.12.2021

(EriveltonMoraes) #1

solvable models ( 3 , 43 ) and classical numer-
ical methods ( 35 , 36 ).
Note added in proof: During the completion
of this manuscript, we became aware of re-
lated work demonstrating the preparation of
toric code states by using quantum circuits on
a superconducting processor ( 44 ).


REFERENCES AND NOTES



  1. X.-G. Wen,Rev. Mod. Phys. 89 , 041004 (2017).

  2. S. Sachdev,Rep. Prog. Phys. 82 , 014001 (2019).

  3. A. Kitaev,Ann. Phys. 303 ,2–30 (2003).

  4. C. Nayak, S. H. Simon, A. Stern, M. Freedman, S. Das Sarma,
    Rev. Mod. Phys. 80 , 1083–1159 (2008).

  5. L. Savary, L. Balents,Rep. Prog. Phys. 80 , 016502 (2017).

  6. D. S. Rokhsar, S. A. Kivelson,Phys. Rev. Lett. 61 , 2376–2379 (1988).

  7. N. Read, S. Sachdev,Phys. Rev. Lett. 66 , 1773–1776 (1991).

  8. S. Sachdev,Phys. Rev. B Condens. Matter 45 , 12377–12396 (1992).

  9. R. Moessner, S. L. Sondhi,Phys. Rev. Lett. 86 , 1881–1884 (2001).

  10. G. Misguich, D. Serban, V. Pasquier,Phys. Rev. Lett. 89 ,
    137202 (2002).

  11. B. I. Halperin, J. K. Jain,Fractional Quantum Hall Effects
    (World Scientific, 2020).

  12. T.-H. Hanet al.,Nature 492 , 406–410 (2012).

  13. M. Fu, T. Imai, T.-H. Han, Y. S. Lee,Science 350 , 655–658 (2015).

  14. C. Gross, I. Bloch,Science 357 , 995–1001 (2017).

  15. H. Weimer, M. Müller, I. Lesanovsky, P. Zoller, H. P. Büchler,
    Nat. Phys. 6 , 382–388 (2010).

  16. M. Hermele, V. Gurarie, A. M. Rey,Phys. Rev. Lett. 103 , 135301
    (2009).

  17. N. Y. Yaoet al.,Phys. Rev. Lett. 110 , 185302 (2013).

  18. A. W. Glaetzleet al.,Phys. Rev. X 4 , 041037 (2014).

  19. A. Celiet al.,Phys. Rev. X 10 , 021057 (2020).

  20. S. de Léséleucet al.,Science 365 , 775–780 (2019).

  21. T. Ozawaet al.,Rev. Mod. Phys. 91 , 015006 (2019).

  22. R. Samajdar, W. W. Ho, H. Pichler, M. D. Lukin, S. Sachdev,
    Proc. Natl. Acad. Sci. U.S.A. 118 , e2015785118 (2021).

  23. R. Verresen, M. D. Lukin, A. Vishwanath,Phys. Rev. X 11 ,
    031005 (2021).

  24. C. Songet al.,Phys. Rev. Lett. 121 , 030502 (2018).

  25. C. K. Andersenet al.,Nat. Phys. 16 , 875–880 (2020).

  26. S. Zhou, D. Green, E. D. Dahl, C. Chamon,Phys. Rev. B 104 ,
    L081107 (2021).

  27. M. Saffman, T. G. Walker, K. Mølmer,Rev. Mod. Phys. 82 ,
    2313 – 2363 (2010).

  28. P. Anderson,Mater. Res. Bull. 8 , 153–160 (1973).

  29. S. Ebadiet al.,Nature 595 , 227–232 (2021).

  30. P. Schollet al.,Nature 595 , 233–238 (2021).

  31. Materials and methods are available as supplementary materials.

  32. K. Fredenhagen, M. Marcu,Commun. Math. Phys. 92 , 81– 119
    (1983).

  33. J. Bricmont, J. Frölich,Phys. Lett. B 122 , 73–77 (1983).

  34. K. Gregor, D. A. Huse, R. Moessner, S. L. Sondhi,New J. Phys.
    13 , 025009 (2011).

  35. S. R. White,Phys. Rev. Lett. 69 , 2863–2866 (1992).

  36. J. Hauschild, F. Pollmann,SciPost Phys. Lect. Notesp. 5 (2018).

  37. M. P. Zaletel, R. S. K. Mong, C. Karrasch, J. E. Moore,
    F. Pollmann,Phys. Rev. B Condens. Matter Mater. Phys. 91 ,
    165112 (2015).

  38. M. B. Hastings, X.-G. Wen,Phys. Rev. B Condens. Matter Mater.
    Phys. 72 , 045141 (2005).

  39. I. Cong, S. Choi, M. D. Lukin,Nat. Phys. 15 , 1273–1278 (2019).

  40. E. Dennis, A. Kitaev, A. Landahl, J. Preskill,J. Math. Phys. 43 ,
    4452 – 4505 (2002).

  41. M. C. Bañulset al.,Eur. Phys. J. D 74 , 165 (2020).

  42. L. Tagliacozzo, A. Celi, A. Zamora, M. Lewenstein,Ann. Phys.
    330 , 160–191 (2013).

  43. A. Kitaev,Ann. Phys. 321 ,2–111 (2006).

  44. K. J. Satzingeret al.,Science 374 , 1237–1241 (2021).

  45. G. Semeghiniet al., Probing topological spin liquids on a
    programmable quantum simulator.Harvard Dataverse(2021);
    doi:10.7910/DVN/BDCTRX.

  46. R. Verresen, rubenverr/DMRG-QSL-Rydberg-experiment.
    Zenodo (2021); doi:10.5281/zenodo.5553679.


ACKNOWLEDGMENTS
We thank many members of the Harvard AMO community,
particularly E. Urbach, S. Dakoulas, and J. Doyle for their efforts
enabling operation of our laboratories during 2020-2021. We thank
S. Choi, I. Cong, E. Demler, G. Giudici, W. W. Ho, N. Maskara,
K. Najafi, N. Yao, and S. Yelin for stimulating discussions.Funding:


We acknowledge financial support from the Center for Ultracold
Atoms, the National Science Foundation, the U.S. Department
of Energy (DE-SC0021013 and LBNL QSA Center), the Army
Research Office MURI, the DARPA ONISQ program, QuEra
Computing, and Amazon Web Services. We further acknowledge
support from the Max Planck/Harvard Research Center for
Quantum Optics fellowship (to G.S.), the National Defense Science
and Engineering Graduate (NDSEG) fellowship (to H.L.), Gordon
College (to T.T.W,), the NSF Graduate Research Fellowship
Program (grant DGE1745303) and The Fannie and John Hertz
Foundation (to D.B.), the Harvard Quantum Initiative Postdoctoral
Fellowship in Science and Engineering (to R.V.), the Simons
Collaboration on Ultra-Quantum Matter (Simons Foundation grant
651440 to R.V., A.V., and S.S.). R.S. and S.S. were supported
by the U.S. Department of Energy under grant DE-SC0019030.
The DMRG simulations were performed by using the Tensor
Network Python (TeNPy) package developed by J. Hauschild and
F. Pollmann ( 36 ) and were run on the FASRC Cannon and Odyssey
clusters supported by the FAS Division of Science Research
Computing Group at Harvard University.Author contributions:
G.S., H.L., A.K., S.E., T.T.W., D.B., and A.O. contributed to building
the experimental setup, performed the measurements, and
analyzed the data. R.V., H.P., and A.V. contributed to developing

methods for detecting QSL correlations, performed numerical
simulations, and contributed to the theoretical interpretation of the
results. M.K. and R.S. contributed to the theoretical interpretation
of the results. All work was supervised by S.S., M.G., V.V., and
M.D.L. All authors discussed the results and contributed to the
manuscript.Competing interests:M.G., V.V., and M.D.L. are
cofounders and shareholders of QuEra Computing. A.K. is CEO and
shareholder of QuEra Computing. A.O. is shareholder of QuEra
Computing. Some of the techniques and methods used in this work
are included in provisional and pending patent applications filed by
Harvard University (US patent application nos. 16/630719, 63/116,321,
and 63/166,165).Data and materials availability:The data and
code are available on Harvard Dataverse ( 45 ) and Zenodo ( 46 ).

SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.abi8794
Materials and Methods
Figs. S1 to S19
References ( 47 – 56 )

8 April 2021; accepted 28 October 2021
10.1126/science.abi8794

PLANT SCIENCE

Ground tissue circuitry regulates organ complexity in


maize andSetaria


Carlos Ortiz-Ramírez1,2, Bruno Guillotin^1 †, Xiaosa Xu^3 †, Ramin Rahni^1 †, Sanqiang Zhang^1 , Zhe Yan^4 ‡,
Poliana Coqueiro Dias Araujo^1 §, Edgar Demesa-Arevalo^3 , Laura Lee^1 , Joyce Van Eck5,6,
Thomas R. Gingeras^3 , David Jackson^3 , Kimberly L. Gallagher^4 , Kenneth D. Birnbaum^1 *

Most plant roots have multiple cortex layers that make up the bulk of the organ and play key roles
in physiology, such as flood tolerance and symbiosis. However, little is known about the formation
of cortical layers outside of the highly reduced anatomy ofArabidopsis. Here, we used single-cell
RNA sequencing to rapidly generate a cell-resolution map of the maize root, revealing an alternative
configuration of the tissue formative transcription factor SHORT-ROOT (SHR) adjacent to an
expanded cortex. We show that maize SHR protein is hypermobile, moving at least eight cell layers
into the cortex. Higher-orderSHRmutants in both maize andSetariahave reduced numbers of
cortical layers, showing that theSHRpathway controls expansion of cortical tissue to elaborate
anatomical complexity.

R


oots are radially symmetrical organs com-
posed of three fundamental tissue types:
the epidermis on the outside, the ground
tissue at the middle, and a core of vascular
elements plus pericycle that lie in a cen-
tral cylinder known as the stele ( 1 ). The ground

tissue is further divided into two different cell
types, the endodermis and cortex, which are
arranged as concentric layers around the stele.
Variations in ground tissue patterning, partic-
ularly the number of cortex cell layers, are
common across species and represent one of
the defining features giving rise to interspecies
root morphological diversity ( 1 ). This diversity
allows plants to cope with biotic and abiotic
stress and adapt to challenging environments.
For example, maize cortex plays a role in drought
and flood tolerance and hosts colonization of
beneficial mycorrhizal associations that reduce
stress and improve nutrient uptake ( 2 – 5 ). There-
fore, an ongoing question in root biology is how
tissue patterning is adjusted to produce diver-
gent root morphologies, and what alterations in
the genetic networks control developmental dif-
ferences among species.
A current limitation is that knowledge of
radial patterning mechanisms in roots comes
largely from the study ofArabidopsis, which

SCIENCEscience.org 3 DECEMBER 2021¥VOL 374 ISSUE 6572 1247


(^1) Center for Genomics and Systems Biology, Department of
Biology, New York University, New York, NY 10003, USA.
(^2) UGA Laboratorio Nacional de Genómica para la
Biodiversidad, CINVESTAV Irapuato, Guanajuato 36821,
México.^3 Cold Spring Harbor Laboratory, Cold Spring Harbor,
NY 11724, USA.^4 School of Arts and Sciences, University of
Pennsylvania, Philadelphia, PA 1904, USA.^5 Boyce Thompson
Institute, Ithaca, NY 14853, USA.^6 Plant Breeding and
Genetics Section, School of Integrative Plant Science, Cornell
University, Ithaca, NY 14853, USA.
*Corresponding author. Email: [email protected]
†These authors contributed equally to this work.
‡Present address: The National Key Facility for Crop Gene Resources
and Genetic Improvement (NFCRI)/Key Lab of Germplasm Utilization
(MOA), Institute of Crop Sciences, Chinese Academy of Agricultural
Sciences, Beijing 100081, China.
§Present address: Department of Agronomic and Forest Sciences,
Universidade Federal Rural do Semi-Árido, RN, Brazil.
RESEARCH | RESEARCH ARTICLES

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